Abstract
The Advancement in Game Technology Has Served to Enrich Player's Gaming Experience in a Substantial Way. Nowadays, it is Common to Have Blockbuster Quality Games, with Realistic Graphics and Engaging Stories. Despite This, the Progress Made in Incorporating Artificial Intelligence Has Been Slow, and Realistic Human-Like Intelligence in Games is Hardly to Be Found. There Have Been Some Attempts to Use Machine Learning in Games, But Such Attempts Often Ended Up Impractical or Affecting the Players Enjoyment Due to Several Constraining Factors. in This Paper, We Describe Meme War as a Proof-Of-Concept for Practical Usage of Machine Learning in Games. We Introduce Extreme Learning Machine (Elm) as One Approach to Achieve a Better Experience in Employing Machine Learning in Games. Advantages of Elm over Multilayer Perceptron (MLP) Are Presented in Terms of What Elm Can Offer in a Practical Point of View of the Player.
Recommended Citation
Y. Miche et al., "Meme Representations for Game Agents," World Wide Web, vol. 18, no. 2, pp. 215 - 234, Springer, Mar 2015.
The definitive version is available at https://doi.org/10.1007/s11280-013-0219-3
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
Extreme learning machine; Game agents; Memetic computing; Video games
International Standard Serial Number (ISSN)
1573-1413; 1386-145X
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Springer, All rights reserved.
Publication Date
01 Mar 2015
